Apache Cassandra

Definition

Apache Cassandra is a highly scalable, distributed NoSQL database designed to handle large amounts of data across many servers without a single point of failure. It excels in providing high availability and fault tolerance, making it a popular choice for applications requiring massive data storage and real-time analytics.

5 Must Know Facts For Your Next Test

1. Cassandra is designed to handle huge volumes of writes and can scale horizontally by adding more nodes to the cluster without downtime.
2. It uses a peer-to-peer architecture, where each node in the cluster is equal, eliminating any single point of failure and allowing for continuous operation even if some nodes fail.
3. Data in Cassandra is stored in a format similar to a table but allows for flexible schemas, making it easy to accommodate changes in data structure over time.
4. The database employs a tunable consistency model, giving developers the ability to balance between consistency and availability based on application needs.
5. Cassandra's write and read performance remains efficient due to its log-structured storage mechanism and support for partitioning, which optimizes data distribution across nodes.

Review Questions

• How does Apache Cassandra ensure high availability and fault tolerance in a distributed system?
  • Apache Cassandra ensures high availability and fault tolerance through its peer-to-peer architecture where all nodes are equal. This design means that even if some nodes fail, the system continues to operate without interruption. Additionally, data replication across multiple nodes helps prevent data loss and allows read/write operations to occur on any node, thus maintaining availability regardless of node status.
• Discuss the advantages of using Cassandra's tunable consistency model compared to traditional databases.
  • Cassandra's tunable consistency model allows developers to choose the level of consistency required for each operation, which can be adjusted based on specific application needs. This flexibility contrasts with traditional databases that often have a rigid consistency model. By allowing options like eventual consistency or strong consistency depending on the use case, developers can optimize performance while still ensuring data accuracy when necessary.
• Evaluate the implications of Apache Cassandra's scalability features on large-scale data analytics applications.
  • Apache Cassandra's scalability features significantly enhance large-scale data analytics applications by enabling them to handle vast amounts of data efficiently. As organizations generate more data, Cassandra allows for easy horizontal scaling by simply adding more nodes to the cluster without downtime. This capability not only ensures consistent performance during high demand but also provides the necessary infrastructure for real-time analytics across distributed datasets, facilitating timely insights into business operations and customer behaviors.

Related terms

NoSQL: A category of database management systems that do not use SQL as their primary interface, often designed to handle unstructured or semi-structured data.

Distributed Database: A database that is spread across multiple physical locations or servers, allowing for improved redundancy, performance, and availability.

Big Data: Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.